The objective of this proposal, which is designed to address the ever increasing need for integrated knowledge discovery in biology and medicine, is to enable the discovery, verification, and validation of hypotheses concerning interrelationships between image-based, phenotypic, and bio-molecular features in heterogeneous data sets by leveraging multiple conceptual knowledge sources - ultimately supporting """"""""high throughput"""""""" knowledge-driven translational science. To provide for a manageable project scope, Osteoarthritis Initiative (OAI) data sets will be used as a primary, motivating use case for the development and evaluation of the projected research products. This project necessarily involves analysis of initial hypotheses by subject matter experts (SMEs) for system training and verification. However, the ultimate goal of our proposed approach is to minimize the need for human intervention to identify or validate knowledge-anchored hypotheses. In order to generate such hypotheses, four interrelated knowledge sources are used: 1) full-text published bio-medical literature accessed by both conventional text mining and NLP analyses of articles as found in the Medline database and associated full text repositories;2) publically available ontologies included in the National Library of Medicine's Unified Medical Language System (UMLS);3) one or more databases containing phenotypic and functional (e.g. quality of life, psychological, strength and performance measures) data;and 4) computerized-image analysis derived features (e.g. cross-sectional area of the quadriceps).
Public Health Relevance Statement Clinical and translational studies produce heterogeneous sources of data. For example, the Cancer Genome Atlas (TCGA) project is collecting tumor biospecimens together with clinical and histopathological data in order to understand the molecular basis of cancer through the application of genome analysis technologies. Similarly, the Osteoarthritis Initiative (OAI), a multi-center, longitudinal study is designed to assess the incidence and progression of knee osteoarthritis (OA) by collecting anthropometric, biochemical, genetic, and imaging procedures from 4796 enrollees. In these and many other similar studies, the collected data are made publicly available, yet, there is an acute lack of sufficient biomedical informatics tools to effectively discover, verify and validate hypotheses based upon the contents of these heterogeneous data sources. Given these characteristics of the modern research environment, an essential biomedical informatics challenge is to apply knowledge-anchored reasoning to heterogeneous and multi-dimensional data sets in order to discover novel hypotheses concerning such data that may be tested in order to maximize clinical impact and ultimately support broad public health interventions.
| Payne, Philip R O; Jackson, Rebecca D; Best, Thomas M et al. (2012) Applying knowledge-anchored hypothesis discovery methods to advance clinical and translational research: the OAMiner project. J Am Med Inform Assoc 19:1110-4 |
| Ababneh, Sufyan Y; Prescott, Jeff W; Gurcan, Metin N (2011) Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research. Med Image Anal 15:438-48 |
| Prescott, Jeffrey W; Best, Thomas M; Swanson, Mark S et al. (2011) Anatomically anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative. J Digit Imaging 24:28-43 |
| Payne, Philip R O; Borlawsky, Tara B; Lele, Omkar et al. (2011) The TOKEn project: knowledge synthesis for in silico science. J Am Med Inform Assoc 18 Suppl 1:i125-31 |
| Swanson, M S; Prescott, J W; Best, T M et al. (2010) Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees. Osteoarthritis Cartilage 18:344-53 |
| Prescott, Jeffrey W; Priddy, Mike; Best, Thomas M et al. (2009) An automated method to detect interstitial adipose tissue in thigh muscles for patients with osteoarthritis. Conf Proc IEEE Eng Med Biol Soc 2009:6360-3 |
| Prescott, Jeffrey W; Pennell, Michael; Best, Thomas M et al. (2009) An automated method to segment the femur for osteoarthritis research. Conf Proc IEEE Eng Med Biol Soc 2009:6364-7 |
| Gurcan, Metin N; Boucheron, Laura E; Can, Ali et al. (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147-71 |
